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Investor deck

Corvus is the operating system for an AI-native development agency.

Traditional agencies scale by adding humans. Corvus is built to scale client delivery by increasing AI execution capacity inside one harness and moving humans into supervision, review, and exceptions.

Linear work graph
Convex control plane
Daytona execution
GitHub review surface
1
Harness

One runtime instead of disconnected agent tools.

0->1
Closed loop

This review branch turns execution, review, and acceptance into one system.

Category shift

The shift is from human-headcount-scaled agencies to harness-scaled agencies.

Traditional agency
  • More clients means more people.
  • Coordination overhead compounds faster than output.
  • Review, continuity, and PM work stay human bottlenecks.
Corvus model
  • More work means more runs, not proportionally more humans.
  • Execution, review, and acceptance share one runtime.
  • Humans act as conductors, not line-by-line implementers.
The bottleneck

The scarce resource is not coding throughput. It is human coordination.

Work
Graph

Without a durable work graph, agent output is hard to continue or supervise.

Review
Trust

Without durable review and evidence, output is hard to accept with confidence.

State
Continuity

Without one system of record, every handoff becomes expensive human glue.

What the harness does

Corvus turns planning, execution, review, deployment, and acceptance into one loop.

Work graph

Typed project and continuation work, anchored in Linear.

Execution

Sandboxed runs in Daytona with one durable control plane.

Review

Internal pre-PR review and GitHub App review on the live head.

Verification

Runs can test real flows and preserve proof artifacts, not just logs.

Evidence

Acceptance decisions based on proof, not commentary.

Operator control

One surface for runtime, review, and blocked-state visibility.

How Corvus works

Five systems share one runtime instead of behaving like separate tools.

Linear
Durable work graph and continuation contract.
Convex
Durable control plane, queues, evidence, and state.
Daytona
Isolated execution plane for long-running work.
GitHub
PR and review surface, visible where engineers already work.
Vercel
Hosted deployment surface and preview verification.
Harness UI
Operator surface for supervision and diagnosis.
Runtime sequence
1

Linear defines the work to do.

2

Convex records and leases the run.

3

Daytona executes the work in isolation.

4

GitHub receives the PR and review surface.

5

Harness reconciles evidence and acceptance on the latest head.

Why review matters

Review is not decoration on a PR. Review is part of the acceptance contract.

What changed
  • Internal pre-PR review runs inside the delivery loop.
  • GitHub App review runs on the live PR head.
  • Findings and prepared changes are durable.
  • Acceptance only counts when review matches the current head.
Why it matters

This is the branch where Corvus stops treating review as commentary and starts treating it as evidence. That closes the loop between execution, review, and acceptance.

The same loop should extend to verification: agents should be able to test the app, capture proof, and attach the artifact back to the same delivery record.

What this PR proved

Corvus can carry a generated project through execution, review, deploy, and acceptance on one head.

Repo created
PR created
CI green
Corvus Review green
Deploy reached a live 200
Evidence reconciled on the same head
Harness path is ready for richer proof artifacts such as screenshots and videos
Where Corvus fits

Corvus belongs to the same frontier category, but with an agency-first center of gravity.

OpenAI

Harness philosophy: the leverage comes from the system around the model.

Stripe

End-to-end coding agents that can own more of the delivery loop.

Spotify

Background coding-agent systems and stronger feedback loops.

Ramp

Background autonomy plus exception routing as an operating model.

Cursor

Long-running execution and self-driving codebase direction.

Ona / Open SWE

Software as an industrial system and an open reference architecture.

Why Corvus is different

Most systems stop at coding agents. Corvus is aimed at running an AI-native agency.

Agency-first

Built around client delivery economics, not just internal developer productivity.

Proposal plus software

The same harness is designed to span government contract work and software delivery.

One harness

The system is designed to reduce both human and AI sprawl by centralizing the runtime.

What is proven now

The review-and-evidence loop is real. The system is no longer hypothetical.

GitHub review is real and current-head aligned.
Internal pre-PR review feeds acceptance evidence.
Prepared changes and apply flows are durable.
Operators can see runtime, review, and evidence state in one system.
Greenfield proof is complete end to end.
Adjacent teams can now reuse one review primitive instead of inventing their own.
What comes next

The next parity steps are about harness maturity, not changing the thesis.

Machine identity

Replace founder fallback with a dedicated machine user for GitHub and Vercel.

Linear continuation

Upgrade Linear from work graph to real continuation surface for active work.

Operator maturity

Expose blocked states, continuation visibility, and stronger runtime governance.

Closing

One harness. More AI execution. Less human coordination.

Corvus is building the runtime for an AI-native development agency. The point is not to ship more bots. The point is to centralize work, execution, review, and evidence so a small team can supervise a much larger amount of software and proposal delivery.

Category

AI-native development agency harness

A shared runtime that manages project work, execution, review, and acceptance across client software and proposal lifecycles.